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Cinematography Zone System

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geegl
cinematography-zone-system

用户提到分区曝光、定位、第V区、中灰18%、我想让这个物体看起来更亮/更暗

Overview

Publishergeegl
Repositorydirectorskills
Skill namecinematography-zone-system
Stars
68
Forks
7
Bundled files
1
LicenseMIT
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by geegl on GitHub. Read the source before you install it.

Installation

Install the Cinematography Zone System AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/geegl/directorskills/tree/main/output/books/cinematography-brown/cinematography-zone-system
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/geegl/directorskills.git /tmp/directorskills
mkdir -p .claude/skills
cp -r /tmp/directorskills/output/books/cinematography-brown/cinematography-zone-system .claude/skills/cinematography-zone-system
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cinematography Zone System in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cinematography Zone System on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cinematography Zone System is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

分区曝光系统

R — 原文 (Reading)

入射读数、平均反射率18%、灰卡上点测光表的读数、第V区——以上表达的都是一回事，只不过是以不同的角度看问题而已。 — Brown, 第10章, p.g_0042

I — 方法论骨架 (Interpretation)

分区曝光系统（Zone System）由安塞尔·亚当斯发明，将亮度范围分为0-X区（11级），每区相差一档曝光：

  • 第0区：纯黑，无细节
  • 第I区：接近纯黑，略有质感
  • 第V区：中灰（18%反射率）= 入射测光读数 = 灰卡读数
  • 第X区：纯白，无细节

核心操作只有两步：

  1. 定位（Place）：决定画面中某个物体应该落在哪个分区，然后调整曝光使它出现在那里。
  2. 跟随（Fall）：一旦你将某个物体定位到目标分区，场景中所有其他物体将按它们与被定位物体之间的亮度差，自动落入对应的分区。

这个框架将曝光从"太亮了/太暗了"的技术判断，转化为"我希望这个物体看起来多亮"的创意决策。你不是在"正确曝光"，而是在"设计亮度关系"。

A1 — 书中的应用 (Past Application)

案例：窗帘定位到第VIII区

  • 问题：用10K灯模拟阳光射入窗户，窗帘测光读数f/11。导演希望窗帘"很热但不过曝"。
  • 方法论的使用：将窗帘定位到第VIII区（比中灰高3档）。f/11往下数3档=f/4。镜头设定f/4，窗帘在成品中将是"很热的白色"。
  • 结论：定位让你精确控制"热到什么程度"——第VII区是浅灰，第VIII区是很热但有细节，第IX区是接近过曝。
  • 结果：演员站在f/2.8处（入射读数），跟随到第IV区（比中灰暗1档）。如果需要演员正常曝光，必须改变灯光比而非光圈。

案例：点测光表与分区转盘

  • 问题：场景中没有中灰物体，如何确定曝光？
  • 方法论的使用：在演员脸上测到EV10，典型亮色皮肤在第VI区。转动分区转盘让10对准第VI区，读取第V区的曝光值即为镜头应设光圈。
  • 结论：分区系统让你可以从任何已知亮度的物体反推曝光，而不依赖灰卡或入射测光表。

A2 — 触发场景 (Future Trigger) ★

用户会在什么情境下需要这个 skill?

  1. 用户想精确控制画面中某个元素的亮度——"我希望背景是浅灰色而不是白色"
  2. 用户在高对比度场景中需要决定保留哪部分细节——"窗户过曝了怎么办？"
  3. 用户想理解为什么测光表给出的读数和他想拍的效果不一致

语言信号

  • "分区曝光"、"Zone System"、"第V区"
  • "我想让这个更亮/更暗"、"背景应该是灰色的"
  • "测光表读数"、"中灰18%"、"灰卡"
  • "高光不过曝"、"暗部有细节"

与相邻 skill 的区分

  • 与 hard-soft-light 的区别：硬光/软光影响的是对比度范围（亮暗差多大），分区系统决定的是"这个差值在成品中呈现为什么样子"。
  • 与 three-point-lighting 的区别：三点布光解决"灯放哪里"，分区系统解决"最终看起来多亮"。

E — 可执行步骤 (Execution)

  1. 判断场景中关键物体的目标分区

    • 亮色皮肤通常在第VI区，中灰物体在第V区
    • 白色但有细节的物体（如窗帘）在第VII-VIII区
    • 完成标准：能回答"这个物体在成品中应该有多亮？"

  2. 用点测光表测量并定位

    • 测量目标物体的读数，将其定位到目标分区
    • 计算偏移量：如果读数是f/8，目标是第VII区（比第V区高2档），则光圈应为f/4
    • 完成标准：光圈计算正确

  3. 检查跟随结果

    • 场景中其他物体将按亮度差落入对应分区

    • 如果跟随结果不满意（如演员太暗），需要改变灯光比而非光圈

    • 完成标准：所有关键元素的亮度关系符合叙事需求

    • 🔴 判停条件: 若定位后其他关键元素的跟随结果不在可接受的分区内（如演员落入第II区以下），需要改变灯光比而非继续调整光圈

B — 边界 (Boundary) ★

不要在以下情况使用此 skill

  • 用户只是想"正常曝光"而不需要精确控制亮度关系
  • 用户在使用全自动模式拍摄（分区系统需要手动控制曝光）
  • 用户在讨论后期调色而非前期曝光

作者在书中警告的失败模式

  • 只改变光圈的陷阱：如果将窗帘定位到第VIII区后发现演员太暗，不能只调光圈（那会改变窗帘的定位），必须改变灯光比。
  • 忽略跟随：定位一个物体后，必须检查所有其他物体的跟随结果，否则可能出现意料之外的亮度。
  • 过度依赖测光表：测光表假设所有物体都是18%中灰，高反射率物体（白色婚纱）和低反射率物体（黑色西装）会欺骗测光表。

作者的盲点 / 时代局限

Blain Brown 的分析框架源自传统胶片摄影，对数字传感器的特性（如动态范围、高光滚降、暗部噪声）讨论不足。他假设摄影团队有充足的设备和时间，独立制作或低预算场景的应对策略覆盖较少。案例库以好莱坞和欧洲艺术片为主，对亚洲、拉美等非西方电影传统的摄影风格涉及有限。

此外，Zone System 源自安塞尔·亚当斯的胶片摄影体系，Brown 对其在数字传感器上的等效应用讨论不够深入。数字摄影的线性响应曲线与胶片的对数曲线有本质差异，直接套用 Zone System 可能导致曝光策略偏差。此外，他未充分讨论 LOG 拍摄模式对 Zone System 思维的替代作用。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Cinematography Zone System AI skill do?

用户提到分区曝光、定位、第V区、中灰18%、我想让这个物体看起来更亮/更暗

Why use Cinematography Zone System on TypingMind?

Because you install it once and use it with any model. Cinematography Zone System is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cinematography Zone System in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/geegl/directorskills/tree/main/output/books/cinematography-brown/cinematography-zone-system. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Cinematography Zone System?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cinematography Zone System?

As many as you like. As long as a model supports skills, you can use Cinematography Zone System with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cinematography Zone System AI skill free?

Yes. It is published on GitHub by geegl under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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